{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib\n",
    "\n",
    "A Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.\n",
    "\n",
    "Resources:\n",
    "* Offical tutorial: <https://matplotlib.org/tutorials/index.html>\n",
    "    * Especially the [Pyplot tutorial](https://matplotlib.org/tutorials/introductory/pyplot.html)\n",
    "    * You should make the best of the [API manual](https://matplotlib.org/api/) which is very detailed\n",
    "    * Also, matplotlib has a [gallery](https://matplotlib.org/gallery.html) with lots of sample plots you can have a look and learn the codes\n",
    "* Tutorial in Chinese: <https://liam.page/2014/09/11/matplotlib-tutorial-zh-cn/>\n",
    "    * It uses another interface `pylab`, which exists some differences with standard `matplotlib.pyplot`\n",
    "    * But as announced in matplotlib official website, \"pylab is deprecated and its use is strongly discouraged because of namespace pollution. Use pyplot instead.\"\n",
    "\n",
    "    \n",
    "Firstly import the packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parts of a Figure\n",
    "\n",
    "The figure below shows the basic elements in matplotlib.\n",
    "![anatomy](https://matplotlib.org/_images/anatomy.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can follow the anatomies and create our first figure.\n",
    "\n",
    "\n",
    "## Simple Plot\n",
    "\n",
    "We create a numpy array and draw the figure of linear, quadratic, cubic functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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52IxpwldKnTZjDI//8Dj/zPwnk/tO5uZeN3s7pIapqYZVf4EFM6GiyFqQZPQM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\nn/h/9NDuVVVTWMMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16b6a8dcf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create x array (matplotlib also supports Python array)\n",
    "x = np.linspace(0, 2, 100)\n",
    "\n",
    "# create y array\n",
    "y_linear = x\n",
    "y_quad = x ** 2\n",
    "y_cubic = x ** 3\n",
    "\n",
    "# directly plot by plt\n",
    "plt.plot(x, y_linear, label=\"linear\") # labels will be shown in legend\n",
    "plt.plot(x, y_quad, label=\"quadractic\")\n",
    "plt.plot(x, y_cubic, label=\"cubic\")\n",
    "\n",
    "# add x,y axis labels\n",
    "plt.xlabel(\"x label\")\n",
    "plt.ylabel(\"y label\")\n",
    "\n",
    "# add title\n",
    "plt.title(\"Simple Plot\")\n",
    "\n",
    "# show legends\n",
    "plt.legend()\n",
    "\n",
    "# show the final figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can find the arguments of `plt.plot` from the [API manual](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot).\n",
    "Some commonly used arguments are\n",
    "* label\n",
    "* marker: '.', ',', 'o', 'v', '^', '+', 'x'\n",
    "* linestyle: '-', '--', '-.', ':'\n",
    "* alpha\n",
    "    * abbr: 'b', 'g', 'r', 'c', 'm', 'y', 'k', 'w'\n",
    "    * full name: 'green'\n",
    "    * hex strings: '#008000'\n",
    "* color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R3xjJ9teSfqrqY6eZc8ROrfhVdIrjOc5aP8c0x7IS2z8h6VZJ10fEN4brR8bysKS7Vd/H\njWNFxLcj4uVy+R5JJ9reqhaOZ2m992Yz4zmND9JVzOwPqvhVeLjT4aIV23xQx+6c/Idy+SIdu3Py\noOrbOVkl5yUqdqCcv2L9mZJOLpe3SvqyatqxUjHnOSPLvyBpT7l8lqSvlnnPLJfPaipnud0FKnb2\nuInxLF9jm9bemfZuHbtz8qFpj2XFnG9WsQ/o8hXrT5N0+sjygqRrGsx59vDfWkXh/Wc5tpXeL9PK\nWd4/nECe1uR4Hpdrai9U7Jn/Ull6t5Tr/kDFrFWSTpH0j+Ub7yFJ20cee0v5uKclXdtwzn+T9N+S\nHikvnynXXy7p8fLN9rikmxrO+YeSnijzPCDprSOP/ZVynJ+R9IEmc5a3f0/SH6143NTGU8Vsqifp\n/1TM+m6SdLOkm8v7Lenj5c/wuKROQ2M5Luetkr458t7sluu3l+P4aPmeuKXhnB8aeW/u0ch/NKu9\nX5rKWW5zo4ovR4w+bqrjudqFIycBIBmOnASAZChuAEiG4gaAZChuAEiG4gaAZChuAEiG4gaAZChu\nAEjm/wFPuvYNN16MvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16b6a8dc5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x[::10], y_cubic[::10], \"^\", color=\"r\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot with keyword strings\n",
    "\n",
    "There are some instances where you have data in a format that lets you access particular variables with strings. For example, with [`numpy.recarray`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.recarray.html#numpy.recarray) or [`pandas.DataFrame`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame).\n",
    "\n",
    "Matplotlib allows you provide such an object with the data keyword argument. If provided, then you may generate plots with the strings corresponding to these variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mY8y9xpjJxpjJ+fn5HRGOisHhnoA7+zuAL469PViu0Xh63JTosNokp3dmowtP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\nUQJFREQ0SqCIiIhGCRQREdHoP459E3MDAVMnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16b6cbc8978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {'a': np.arange(50),\n",
    "        'c': np.random.randint(0, 50, 50),\n",
    "        'd': np.random.randn(50)}\n",
    "data['b'] = data['a'] + 10 * np.random.randn(50)\n",
    "data['d'] = np.abs(data['d']) * 100\n",
    "\n",
    "plt.scatter('a', 'b', c='c', s='d', data=data) # c: color, s: size\n",
    "plt.xlabel('Entry a')\n",
    "plt.ylabel('Entry b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot with categorical variables\n",
    "\n",
    "It is also possible to create a plot using categorical variables. Matplotlib allows you to pass categorical variables directly to many plotting functions. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD9CAYAAACyYrxEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nvSQ1Z+glqTlDL0nN/TcUV1q4IctMaAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16b6cb59400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names = ['group_a', 'group_b', 'group_c']\n",
    "x_pos = [1, 2, 3]\n",
    "values = [1, 10, 100]\n",
    "\n",
    "plt.bar(x_pos, values, tick_label=names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple figures and axes\n",
    "\n",
    "Matplotlib uses a matrix-like notation to indicate which subfigure you are plotting. You can use the [`subplot(nrows, ncols, index, **kwargs)`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot) function to generate several figures.\n",
    "For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oVe0LBFpfNC6QbaGeYRi1iWTm3xgvq+qhsceTsXNbATcDRwCHAzeLSMuATC8h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\nSimllIrx/wEaaYizkpNEJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16b6cc95630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def f(t):\n",
    "    return np.exp(-t) * np.cos(2*np.pi*t)\n",
    "\n",
    "t1 = np.arange(0.0, 5.0, 0.1)\n",
    "t2 = np.arange(0.0, 5.0, 0.02)\n",
    "\n",
    "# generate a canvas\n",
    "plt.figure(figsize=(10,4))\n",
    "\n",
    "# the first figure\n",
    "plt.subplot(121)\n",
    "plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.title(\"1st subfig\")\n",
    "\n",
    "# the second figure\n",
    "plt.subplot(122)\n",
    "plt.plot(t2, np.cos(2*np.pi*t2), 'r--')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.title(\"2nd subfig\")\n",
    "\n",
    "# show the results\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or you can use `pyplot.subplots` to creates a figure and a grid of subplots with a single call, while providing reasonable control over how the individual plots are created. But the name of the member functions may be different. For example, you need to call `set_xlabel` instead of `xlabel` in subfig mode.\n",
    "\n",
    "See the tutorial of [subplots](https://matplotlib.org/3.1.0/gallery/subplots_axes_and_figures/subplots_demo.html) and you can find how to share the axes, titles, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oVe0LBFpfNC6QbaGeYRi1iWTm3xgvq+qhsceTsXNbATcDRwCHAzeLSMuATC8h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\nSimllIrx/wEaaYizkpNEJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16b6cb41080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate a canvas\n",
    "fig, axs = plt.subplots(1,2,figsize=(10,4))\n",
    "\n",
    "# the first figure\n",
    "axs[0].plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n",
    "axs[0].set_xlabel(\"x\")\n",
    "axs[0].set_ylabel(\"y\")\n",
    "axs[0].set_title(\"1st subfig\")\n",
    "\n",
    "# the second figure\n",
    "axs[1].plot(t2, np.cos(2*np.pi*t2), 'r--')\n",
    "axs[1].set_xlabel(\"x\")\n",
    "axs[1].set_ylabel(\"y\")\n",
    "axs[1].set_title(\"2nd subfig\")\n",
    "\n",
    "# show the results\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouped bar chart with labels\n",
    "\n",
    "To draw bars size by size, you need to calculate the x coordinate yourself.\n",
    "Firstly calculate the coordinates of the ticks and then minus/plus width to obtain the coordinates of the bars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LFi3isMMO48gjj2TOnDls3bqVNWvWcP/99zNuXOUPMT/++OP53ve+t214yZIlRexGi3wE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\noMzMLEkOKDMzS5IDyszMkvT/ASMkqPdlSItNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16b6cb568d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Notice you can use LaTeX command in the text of matplotlib\n",
    "labels = ['$G_1$', '$G_2$', '$G_3$', '$G_4$', '$G_5$'] # TeX command!\n",
    "\n",
    "men_means = [20, 34, 30, 35, 27]\n",
    "women_means = [25, 32, 34, 20, 25]\n",
    "\n",
    "x = np.arange(len(labels))  # the label locations\n",
    "width = 0.35  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.bar(x - width/2, men_means, width, label='Men')\n",
    "rects2 = ax.bar(x + width/2, women_means, width, label='Women')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Scores')\n",
    "ax.set_title('Scores by group and gender')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(labels)\n",
    "ax.legend()\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Colors\n",
    "\n",
    "Colors are important in figures. Commonly, you need not change the colors even if you have multiple curves or bars. But if you have special needs, please use the colors in predefined [color map](https://matplotlib.org/tutorials/colors/colormaps.html#sphx-glr-tutorials-colors-colormaps-py) or reference to those in [Flat UI colors](https://flatuicolors.com/).\n",
    "\n",
    "Read [Colors in Scientific Figures](https://www.aje.com/dist/docs/Using_Color_In_Your_Manuscript_Figures.pdf) and pay attention to that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Storage\n",
    "\n",
    "Finally, you need to output your figure. In research papers, the images should be with high resolution, so please save them in vectogram, i.e. `.eps` or `.pdf` format. Notice the `plt.savefig` function should be called before the `plt.show` function.\n",
    "\n",
    "Let's copy the code above and have a try."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['$G_1$', '$G_2$', '$G_3$', '$G_4$', '$G_5$'] # TeX command!\n",
    "\n",
    "men_means = [20, 34, 30, 35, 27]\n",
    "women_means = [25, 32, 34, 20, 25]\n",
    "\n",
    "x = np.arange(len(labels))  # the label locations\n",
    "width = 0.35  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.bar(x - width/2, men_means, width, label='Men')\n",
    "rects2 = ax.bar(x + width/2, women_means, width, label='Women')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Scores')\n",
    "ax.set_title('Scores by group and gender')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(labels)\n",
    "ax.legend()\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.savefig(\"test.pdf\",format=\"pdf\",dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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